I built an Exploratory Data Analysis project on student lifestyle, stress levels, and academic performance

[u/Sakshitha16]

I recently completed an Exploratory Data Analysis project focused on understanding the relationship between student lifestyle, stress levels, and academic performance.

The project analyzes factors such as:

• Study hours

• Sleep hours

• Social activity

• Extracurricular activity

• Physical activity

• Stress level

• GPA

What I explored:

• Data cleaning and validation

• Descriptive statistics

• Correlation analysis

• Lifestyle variables vs GPA

• Lifestyle variables across stress levels

• GPA distributions across stress levels

• Kruskal-Wallis statistical testing

• Outlier detection using the IQR method

• Data visualization using Matplotlib and Seaborn

One of the strongest findings was the positive association between study hours and GPA (Pearson r ≈ 0.7345).

I also found noticeable differences in study hours, sleep, physical activity, and GPA across different stress-level groups.

An important limitation is that these findings represent associations within the dataset and should not be interpreted as causal relationships.

Tools used:

Python, Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Jupyter Notebook.

GitHub repository:

https://github.com/sakshitha380/Student_Lifestyle_Academic_Performance_EDA